{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandelight/.conda/envs/paddle/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from paddle.vision.models import resnet34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = resnet34()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = paddle.rand([1, 3, 224, 224])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandelight/.conda/envs/paddle/lib/python3.7/site-packages/paddle/nn/layer/norm.py:648: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n"
     ]
    }
   ],
   "source": [
    "out = model(x)\n",
    "# out.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1000]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 6.06452376e-02],\n",
       "       [ 4.79310229e-02],\n",
       "       [-8.08016211e-02],\n",
       "       [ 1.12033474e+00],\n",
       "       [ 2.69736052e-01],\n",
       "       [ 5.22099078e-01],\n",
       "       [ 3.35315764e-01],\n",
       "       [ 9.42096472e-01],\n",
       "       [ 1.66798115e+00],\n",
       "       [ 9.83811557e-01],\n",
       "       [-3.40069771e-01],\n",
       "       [-4.16160226e-01],\n",
       "       [-1.16074920e+00],\n",
       "       [-2.32975297e-02],\n",
       "       [-4.87443268e-01],\n",
       "       [-6.59188390e-01],\n",
       "       [ 9.30805624e-01],\n",
       "       [ 2.84770221e-01],\n",
       "       [ 2.30027676e-01],\n",
       "       [-4.76049453e-01],\n",
       "       [-4.71792519e-01],\n",
       "       [ 9.12746906e-01],\n",
       "       [ 6.79178894e-01],\n",
       "       [ 3.30382466e-01],\n",
       "       [-5.34181774e-01],\n",
       "       [ 6.54586613e-01],\n",
       "       [-6.55687690e-01],\n",
       "       [-4.19414401e-01],\n",
       "       [-2.23773226e-01],\n",
       "       [-2.28731595e-02],\n",
       "       [ 1.22380614e+00],\n",
       "       [-4.66028303e-01],\n",
       "       [-6.14117086e-01],\n",
       "       [ 2.99521297e-01],\n",
       "       [ 7.80953348e-01],\n",
       "       [ 7.26066083e-02],\n",
       "       [ 3.86932492e-01],\n",
       "       [-4.21292305e-01],\n",
       "       [ 7.87800789e-01],\n",
       "       [-3.10519010e-01],\n",
       "       [ 8.49470347e-02],\n",
       "       [ 1.00379097e+00],\n",
       "       [-5.88409066e-01],\n",
       "       [-1.17013967e+00],\n",
       "       [ 1.10521162e+00],\n",
       "       [-5.54596245e-01],\n",
       "       [-3.90416980e-01],\n",
       "       [-4.51789379e-01],\n",
       "       [-3.34913656e-02],\n",
       "       [-1.48786426e-01],\n",
       "       [ 8.48226190e-01],\n",
       "       [-6.84907854e-01],\n",
       "       [ 1.37351286e+00],\n",
       "       [-9.14139569e-01],\n",
       "       [ 1.15448344e+00],\n",
       "       [ 1.47333145e+00],\n",
       "       [ 1.05183661e+00],\n",
       "       [ 7.06692874e-01],\n",
       "       [-2.27948427e+00],\n",
       "       [-1.24902391e+00],\n",
       "       [-1.25251138e+00],\n",
       "       [ 1.00101185e+00],\n",
       "       [ 1.75716543e+00],\n",
       "       [ 1.83326459e+00],\n",
       "       [-6.97923183e-01],\n",
       "       [-1.82769692e+00],\n",
       "       [ 1.04154432e+00],\n",
       "       [ 1.78220913e-01],\n",
       "       [-9.76967692e-01],\n",
       "       [-4.00128931e-01],\n",
       "       [-3.28506112e-01],\n",
       "       [-7.35417102e-03],\n",
       "       [-9.01539803e-01],\n",
       "       [ 9.10724521e-01],\n",
       "       [-1.21352065e+00],\n",
       "       [ 5.48889004e-02],\n",
       "       [-1.70963383e+00],\n",
       "       [-6.66785777e-01],\n",
       "       [-1.29285619e-01],\n",
       "       [-5.62195837e-01],\n",
       "       [-1.43448734e+00],\n",
       "       [-7.33340263e-01],\n",
       "       [-4.75117713e-01],\n",
       "       [-6.13180757e-01],\n",
       "       [-5.13011336e-01],\n",
       "       [ 8.31776679e-01],\n",
       "       [-1.10274005e+00],\n",
       "       [-3.48839253e-01],\n",
       "       [ 8.40315759e-01],\n",
       "       [-1.71266842e+00],\n",
       "       [ 1.69674889e-03],\n",
       "       [ 1.17735279e+00],\n",
       "       [-6.03405535e-01],\n",
       "       [-8.39900315e-01],\n",
       "       [ 9.97507870e-01],\n",
       "       [-6.19533718e-01],\n",
       "       [-1.20442279e-01],\n",
       "       [ 6.07856333e-01],\n",
       "       [-3.37857336e-01],\n",
       "       [ 7.84521937e-01],\n",
       "       [ 2.48541206e-01],\n",
       "       [ 4.91615623e-01],\n",
       "       [ 1.32986820e+00],\n",
       "       [ 3.98014396e-01],\n",
       "       [-1.84999049e-01],\n",
       "       [ 1.36255696e-01],\n",
       "       [ 6.36715710e-01],\n",
       "       [-3.76144908e-02],\n",
       "       [ 5.27667284e-01],\n",
       "       [-3.02448645e-02],\n",
       "       [-1.80513430e+00],\n",
       "       [-3.94308776e-01]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = out.numpy()\n",
    "x = x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,  12.,  41., 115., 211., 252., 197., 121.,  39.,   9.]),\n",
       " array([-3.0870724 , -2.516519  , -1.9459656 , -1.3754123 , -0.804859  ,\n",
       "        -0.23430562,  0.33624774,  0.9068011 ,  1.4773544 ,  2.0479078 ,\n",
       "         2.6184611 ], dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAANkElEQVR4nO3dYYjk9X3H8fenmvogCdTgas15dCVcS7SkJyw2kD4wWKqNJZcULOeDVGjgElBIwAc9I9RAObiSxrSUJnCpEh+YmAMTlBraGBFsoIlZg0TP0+aIFz3v8DZJ21gKlju/fbB/yXjuuns7Mze3X98vOGbmN/+Z+f5R3vff/83MpqqQJPXya7MeQJI0ecZdkhoy7pLUkHGXpIaMuyQ1dO6sBwC44IILan5+ftZjSNKm8vjjj/+squZWuu+siPv8/DyLi4uzHkOSNpUkP13tPk/LSFJDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ2tGfckW5M8kuRgkgNJPjWsfzbJi0meGP58aOQxtyY5lOTZJNdMcwckSW+0ng8xnQBuqaofJnkn8HiSh4b7vlBVfzu6cZLLgJ3A5cC7ge8k+e2qOjnJwSVJq1sz7lV1DDg2XH85yUFgy5s8ZAdwb1W9AjyX5BBwJfDvE5hXOuPmdz84k9c9vPe6mbyuejitc+5J5oErgO8PSzcn+VGSu5KcP6xtAV4YedgRVvjLIMmuJItJFpeWlk5/cknSqtYd9yTvAO4DPl1VvwS+BLwH2M7ykf3nX9t0hYe/4Xf5VdW+qlqoqoW5uRW/90aStEHrinuSt7Ec9nuq6hsAVfVSVZ2sqleBL7N86gWWj9S3jjz8EuDo5EaWJK1lPe+WCXAncLCq7hhZv3hks48CTw3XHwB2JjkvyaXANuCxyY0sSVrLet4t8wHgY8CTSZ4Y1j4D3JBkO8unXA4DnwCoqgNJ9gNPs/xOm5t8p4wknVnrebfMd1n5PPq33uQxe4A9Y8wlSRqDn1CVpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1dO6sB5DWY373g7MeQdpUPHKXpIaMuyQ1ZNwlqSHjLkkNGXdJamjNuCfZmuSRJAeTHEjyqWH9XUkeSvLj4fL8kcfcmuRQkmeTXDPNHZAkvdF6jtxPALdU1XuB9wM3JbkM2A08XFXbgIeH2wz37QQuB64FvpjknGkML0la2Zpxr6pjVfXD4frLwEFgC7ADuHvY7G7gI8P1HcC9VfVKVT0HHAKunPDckqQ3cVrn3JPMA1cA3wcuqqpjsPwXAHDhsNkW4IWRhx0Z1k59rl1JFpMsLi0tbWB0SdJq1h33JO8A7gM+XVW/fLNNV1irNyxU7auqhapamJubW+8YkqR1WFfck7yN5bDfU1XfGJZfSnLxcP/FwPFh/QiwdeThlwBHJzOuJGk91vxumSQB7gQOVtUdI3c9ANwI7B0u7x9Z/2qSO4B3A9uAxyY5tPRWMMvv0zm897qZvbYmYz1fHPYB4GPAk0meGNY+w3LU9yf5OPA8cD1AVR1Ish94muV32txUVScnPbgkaXVrxr2qvsvK59EBrl7lMXuAPWPMJUkag59QlaSGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLU0JpxT3JXkuNJnhpZ+2ySF5M8Mfz50Mh9tyY5lOTZJNdMa3BJ0urWc+T+FeDaFda/UFXbhz/fAkhyGbATuHx4zBeTnDOpYSVJ67Nm3KvqUeAX63y+HcC9VfVKVT0HHAKuHGM+SdIGjHPO/eYkPxpO25w/rG0BXhjZ5siwJkk6gzYa9y8B7wG2A8eAzw/rWWHbWukJkuxKsphkcWlpaYNjSJJWsqG4V9VLVXWyql4FvsyvTr0cAbaObHoJcHSV59hXVQtVtTA3N7eRMSRJq9hQ3JNcPHLzo8Br76R5ANiZ5LwklwLbgMfGG1GSdLrOXWuDJF8DrgIuSHIEuB24Ksl2lk+5HAY+AVBVB5LsB54GTgA3VdXJqUwuSVrVmnGvqhtWWL7zTbbfA+wZZyhJ0nj8hKokNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpoTV/zZ6kt5753Q/O5HUP771uJq/bkUfuktSQcZekhoy7JDVk3CWpIf9BVadlVv/QJun0eOQuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJamhNeOe5K4kx5M8NbL2riQPJfnxcHn+yH23JjmU5Nkk10xrcEnS6tZz5P4V4NpT1nYDD1fVNuDh4TZJLgN2ApcPj/liknMmNq0kaV3WjHtVPQr84pTlHcDdw/W7gY+MrN9bVa9U1XPAIeDKyYwqSVqvjZ5zv6iqjgEMlxcO61uAF0a2OzKsSZLOoEn/g2pWWKsVN0x2JVlMsri0tDThMSTprW2jcX8pycUAw+XxYf0IsHVku0uAoys9QVXtq6qFqlqYm5vb4BiSpJVsNO4PADcO128E7h9Z35nkvCSXAtuAx8YbUZJ0utb8PvckXwOuAi5IcgS4HdgL7E/yceB54HqAqjqQZD/wNHACuKmqTk5pdknSKtaMe1XdsMpdV6+y/R5gzzhDSZLG4ydUJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqaFzx3lwksPAy8BJ4ERVLSR5F/B1YB44DPxZVf3neGNKkk7HJI7cP1hV26tqYbi9G3i4qrYBDw+3JUln0DROy+wA7h6u3w18ZAqvIUl6E+PGvYBvJ3k8ya5h7aKqOgYwXF640gOT7EqymGRxaWlpzDEkSaPGOucOfKCqjia5EHgoyTPrfWBV7QP2ASwsLNSYc0iSRox15F5VR4fL48A3gSuBl5JcDDBcHh93SEnS6dlw3JO8Pck7X7sO/BHwFPAAcOOw2Y3A/eMOKUk6PeOclrkI+GaS157nq1X1L0l+AOxP8nHgeeD68ceUJJ2ODce9qn4C/N4K6z8Hrh5nKEnSePyEqiQ1NO67ZTQD87sfnPUIks5yHrlLUkMeuUs6a8zqp9LDe6+byetOk0fuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNnTvrATazWf2mdklai0fuktSQR+6S3vJm+VP44b3XTeV5PXKXpIaMuyQ1ZNwlqSHjLkkNTe0fVJNcC/w9cA7wT1W1d1qv5VsSJen1pnLknuQc4B+BPwYuA25Ictk0XkuS9EbTOi1zJXCoqn5SVf8H3AvsmNJrSZJOMa3TMluAF0ZuHwF+f3SDJLuAXcPN/0ny7ARf/wLgZxN8vrNFx/1ynzaPjvs1833K34z18N9a7Y5pxT0rrNXrblTtA/ZN5cWTxapamMZzz1LH/XKfNo+O+9Vxn14zrdMyR4CtI7cvAY5O6bUkSaeYVtx/AGxLcmmSXwd2Ag9M6bUkSaeYymmZqjqR5GbgX1l+K+RdVXVgGq+1iqmc7jkLdNwv92nz6LhfHfcJgFTV2ltJkjYVP6EqSQ0Zd0lqqG3ck/x1kh8leSLJt5O8e9YzjSvJ55I8M+zXN5P8xqxnmoQk1yc5kOTVJJv6bWlJrk3ybJJDSXbPep5JSHJXkuNJnpr1LJOQZGuSR5IcHP6/+9SsZ5qGtnEHPldV76uq7cA/A38143km4SHgd6vqfcB/ALfOeJ5JeQr4U+DRWQ8yjsZfu/EV4NpZDzFBJ4Bbquq9wPuBm5r8d3qdtnGvql+O3Hw7p3yIajOqqm9X1Ynh5vdY/vzApldVB6tqkp9QnpWWX7tRVY8Cv5j1HJNSVceq6ofD9ZeBgyx/qr6V1r9mL8ke4M+B/wY+OONxJu0vgK/Pegi9zppfu6GzS5J54Arg+zMeZeI2ddyTfAf4zRXuuq2q7q+q24DbktwK3AzcfkYH3IC19mnY5jaWf7S850zONo717FcDa37ths4eSd4B3Ad8+pSf9FvY1HGvqj9c56ZfBR5kE8R9rX1KciPwJ8DVtYk+pHAa/602M792Y5NI8jaWw35PVX1j1vNMQ9tz7km2jdz8MPDMrGaZlOEXoPwl8OGq+t9Zz6M38Gs3NoEkAe4EDlbVHbOeZ1rafkI1yX3A7wCvAj8FPllVL852qvEkOQScB/x8WPpeVX1yhiNNRJKPAv8AzAH/BTxRVdfMdKgNSvIh4O/41ddu7JntRONL8jXgKpa/Hvcl4PaqunOmQ40hyR8A/wY8yXIfAD5TVd+a3VST1zbukvRW1va0jCS9lRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ19P++/kajGvCVegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot\n",
    "pyplot.hist(x)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
